Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: Techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users. The knowledge-graph system builds the knowledge graph using answers produced by an LLM for novel queries. The chatbot will continue to use the LLM to answer novel queries, but the chatbot may harness the knowledge graph to answer repeat questions to gain various efficiencies over LLM-backed chatbots. For example, the knowledge-graph system may easily debug or otherwise improve the answers in knowledge graphs, store provenance information in knowledge graphs, and augment the knowledge graphs using other data sources. Thus, the reliability and correctness of chatbots will be improved as the bugs and inaccuracies in answers provided by the LLM will be corrected in the knowledge graphs, but the chatbots can still harness the abilities of LLMs to provide answers across various subject-matter domains.

Abstract: Techniques implemented by a network-access analysis system to analyze network access controls for networks, identify traffic flows that are unobserved and unrequired, and determine proposed changes to the network access controls that restrict access from unobserved traffic flows. The system may analyze the network access controls, and determine whether unrequired traffic flows are allowed to be communicated in the network. For instance, the system may analyze network flow logs and identify observed traffic flows that are required by applications in the network, and also identify unobserved traffic flows that are permitted access to, but are not observed in, the network. The system may propose changes to the network access controls to restrict network access by these unobserved traffic flows. A network administrator can receive recommendations from the system regarding the proposed changes, and determine whether they would like to implement the proposed changes to their network access controls.

Abstract: Techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users. The knowledge-graph system builds the knowledge graph using answers produced by an LLM for novel queries. The chatbot will continue to use the LLM to answer novel queries, but the chatbot may harness the knowledge graph to answer repeat questions to gain various efficiencies over LLM-backed chatbots. For example, the knowledge-graph system may easily debug or otherwise improve the answers in knowledge graphs, store provenance information in knowledge graphs, and augment the knowledge graphs using other data sources. Thus, the reliability and correctness of chatbots will be improved as the bugs and inaccuracies in answers provided by the LLM will be corrected in the knowledge graphs, but the chatbots can still harness the abilities of LLMs to provide answers across various subject-matter domains.

Abstract: Techniques for a knowledge-graph system to use large language models (LLMs) to build knowledge graphs to answer queries submitted to a chatbot by users. The knowledge-graph system builds the knowledge graph using answers produced by an LLM for novel queries. The chatbot will continue to use the LLM to answer novel queries, but the chatbot may harness the knowledge graph to answer repeat questions to gain various efficiencies over LLM-backed chatbots. For example, the knowledge-graph system may easily debug or otherwise improve the answers in knowledge graphs, store provenance information in knowledge graphs, and augment the knowledge graphs using other data sources. Thus, the reliability and correctness of chatbots will be improved as the bugs and inaccuracies in answers provided by the LLM will be corrected in the knowledge graphs, but the chatbots can still harness the abilities of LLMs to provide answers across various subject-matter domains.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: A network change verification (NCV) system is disclosed for checking whether a proposed configuration change on a network alters the way that the network controls recently observed network flows. In embodiments, the system builds an observed flow control model (OFCM) from logs of recent flows observed in the network. The OFCM, which may be periodically updated based on newly observed flows, provides a compact representation of how individual network flows were ostensibly controlled by the network. When a proposed configuration change is received, the system analyzes the change against the OFCM to check whether the change will alter how the network controls recently observed flows. If so, the proposed change is blocked, and an alert is generated identifying flows that are affected by the change. The NCV system thus prevents network operators from accidentally making changes on the network that will materially alter the behavior of the network.

Abstract: Features are disclosed for managing routing rules stored by a routing device and used to manage network traffic in a network. A computing device can receive multiple routing rules corresponding to multiple routing devices in the network. The computing device can use a formal specification and a snapshot to generate a model of the network. The computing device may use the model in order to statically determine the set of possible paths without causing the transmission of data between a routing device and a destination. the computing device may compare the identified routing rules and the possible paths in order to determine excess routing rules. The computing device may remove the excess routing rules from the routing rules for each routing device such that each routing device routes subsequent network traffic based on the updated routing rules.

Abstract: This disclosure describes techniques for identifying blocked paths and network configuration settings that block paths in networks, such as network paths in a virtual private cloud (VPC). The configuration of virtual networks depends on the correct configuration of many networking resources, such as firewalls, security groups, routing lists, access control lists (ACLs), and the like. In some cases, an analysis that uses formal methods can be performed to determine a network configuration of a virtual network. Using the network configuration information, network paths that are blocked and network configuration settings that may be blocking one or more of the network paths can be determined. The PAS can provide an explanation of what is blocking the network paths. For example, the PAS may identify that a configuration setting of a firewall, router, network gateway, an access control list (ACL), and the like may be blocking a network path.